Microstructural design of solid oxide fuel cell electrodes by micro-modeling coupled with artificial neural network

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dc.contributor.authorTimurkutluk, Bora
dc.contributor.authorCiflik, Yelda
dc.contributor.authorSonugur, Guray
dc.contributor.authorAltan, Tolga
dc.contributor.authorGenc, Omer
dc.contributor.authorColak, Andac Batur
dc.date.accessioned2023-11-07T07:39:11Z
dc.date.available2023-11-07T07:39:11Z
dc.date.issued2023en_US
dc.departmentRektörlük, Bilişim Teknolojileri Uygulama ve Araştırma Merkezien_US
dc.description.abstractArtificial neural network (ANN) is used to model active three/triple phase boundaries (TPBs) in solid oxide fuel cell (SOFC) electrodes composed of phases with various particle sizes for the first time in the literature. Electrode mi crostructures comprising catalyst, electrolyte and pore phases with the same volume fraction, but various mean particle sizes are synthetically generated via Dream.3D software and the active TPB densities are measured by COMSOL software to obtain input data for training the ANN models as well as to validate the network results. In this regard, three learning methods of Bayesian regulation (BR), Levenberg-Marquardt (LM) and Scaled conjugate gradient (SCG) with various hidden layer and neuron numbers are examined. Among ANN models with three inputs and one output, the model with BR including one hidden layer and five neurons performs the best. This model revealing an average relative error of only 0.036 is then employed to simulate SOFC electrodes microstructures with new particle sizes not introduced in the learning process. The active TPB densities estimated by ANN are found to agree well with the computed ones. Therefore, ANN modeling is considered as a useful tool for the prediction of active TPB density in SOFC electrodes after a careful selection of backpropagation method and network structure.en_US
dc.identifier.doi10.1016/j.powtec.2023.118551en_US
dc.identifier.scopus2-s2.0-85153857664en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11467/6915
dc.identifier.urihttps://doi.org/10.1016/j.powtec.2023.118551
dc.identifier.volume425en_US
dc.identifier.wosWOS:000992991400001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofPowder Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Başka Kurum Yazarıen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectSolid oxide fuel cell Electrode design, Synthetic microstructure generation, Three/triple phase boundaries, Artificial neural networken_US
dc.titleMicrostructural design of solid oxide fuel cell electrodes by micro-modeling coupled with artificial neural networken_US
dc.typeArticleen_US

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